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Dynamic Textures (DTs) are sequences of images of moving scenes that exhibitcertain stationarity properties in time such as smoke, vegetation and fire. Theanalysis of DT is important for recognition, segmentation, synthesis orretrieval for a range of applications including surveillance, medical imagingand remote sensing. Deep learning methods have shown impressive results and arenow the new state of the art for a wide range of computer vision tasksincluding image and video recognition and segmentation. In particular,Convolutional Neural Networks (CNNs) have recently proven to be well suited fortexture analysis with a design similar to a filter bank approach. In thispaper, we develop a new approach to DT analysis based on a CNN method appliedon three orthogonal planes x y , xt and y t . We train CNNs on spatial framesand temporal slices extracted from the DT sequences and combine their outputsto obtain a competitive DT classifier. Our results on a wide range of commonlyused DT classification benchmark datasets prove the robustness of our approach.Significant improvement of the state of the art is shown on the largerdatasets.

Convolutional Neural Network on Three Orthogonal Planes for Dynamic Texture Classification